GPGPU |
General-Purpose Computation Using Graphics Hardware
|
IntroductionGPGPU stands for General-Purpose computation on GPUs. With the increasing programmability of commodity graphics processing units (GPUs), these chips are capable of performing more than the specific graphics computations for which they were designed. They are now capable coprocessors, and their high speed makes them useful for a variety of applications. The goal of this page is to catalog the current and historical use of GPUs for general-purpose computation.
|
gDEBugger V4.0 Adds Linux Support and a Buffer Viewer The new gDEBugger V4.0 introduces gDEBugger Linux. This new exciting product adds 32-bit and 64-bit Linux Support, bringing all of gDEBugger's debugging and profiling abilities to the Linux OpenGL developers' world. A new Texture and Buffer Viewer has been added. This Viewer allows you to view textures, static buffers and pbuffers as images or raw data in its original format, including non-RGB data formats (float, depth, integer, luminance, etc). This version also includes significant performance improvements. gDEBugger, an OpenGL and OpenGL ES debugger and profiler, traces application activity on top of the OpenGL API to let programmers see what is happening within the graphics system implementation to find bugs and optimize OpenGL application performance. (http://www.gremedy.com)
Posted: 02 Apr 2008 [GPGPU /Tools] # CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment The Smith-Waterman algorithm has been available for more than 25 years. It is based on a dynamic programming approach that explores all the possible alignments between two biological sequences; as a result it returns the optimal local alignment. Unfortunately, the computational cost is very high, requiring a number of operations proportional to the product of the length of two sequences. This paper by Svetlin Manavski and Giorgio Valle describes SmithWaterman-CUDA, an open-source project to perform fast sequence alignment on the GPU. Although the software performs the optimal Smith-Waterman alignment it is faster than heuristics approaches like FASTA and BLAST. The tests on protein data banks show up to 30x speed up related to reference CPU implementations. (Svetlin A. Manavski, Giorgio Valle, CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment, BMC Bioinformatics 2008, 9(Suppl 2):S10 (26 March 2008))
Posted: 02 Apr 2008 [GPGPU /Scientific Computing] # Relational Joins on Graphics Processors Abstract: "We present a novel design and implementation of relational join algorithms for new-generation graphics processing units (GPUs). Taking advantage of GPU features, we design a set of data-parallel primitives such as split and sort, and use these primitives to implement indexed or non-indexed nested-loop, sort-merge and hash joins. Our algorithms utilize the high parallelism as well as the high memory bandwidth of the GPU, and use parallel computation and memory optimizations to effectively reduce memory stalls. We have implemented our algorithms on a PC with an NVIDIA G80 GPU and an Intel quad-core CPU. Our GPU-based join algorithms are able to achieve a performance improvement of 2-7X over their optimized CPU-based counterparts. (Bingsheng He, Ke Yang, Rui Fang, Mian Lu, Naga K. Govindaraju, Qiong Luo, and Pedro V. Sander. Relational Joins on Graphics Processors. ACM SIGMOD 2008.)
Posted: 02 Apr 2008 [GPGPU /Database] # A SIMD interpreter for Genetic Programming on GPU Graphics Cards Abstract: Mackey-Glass chaotic time series prediction and nuclear protein classification show the feasibility of evaluating genetic programming populations directly on parallel consumer gaming graphics processing units. Using a Linux KDE computer equipped with an nVidia GeForce 8800 GTX graphics processing unit card the C++ SPMD interpretter evolves programs at Giga GP operations per second (895 million GPops). We use the RapidMind general processing on GPU (GPGPU) framework to evaluate an entire population of a quarter of a million individual programs on a non-trivial problem in 4 seconds. An efficient reverse polish notation (RPN) tree based GP is given. (A SIMD interpreter for Genetic Programming on GPU Graphics Cards. W.B. Langdon and W. Banzhaf. In M. Neill, L. Vanneschi, A.I. Esparcia Alcazar, S. Gustafson eds., EuroGP 2008, pp73-85. Springer, LNCS 4971, 26-28 March, Naples.)
Posted: 02 Apr 2008 [GPGPU /Scientific Computing] # |
Categories
GPGPU PeopleFor a list of people doing GPGPU work, See the GPGPU wiki |
